Feature Learning for Chord Recognition: The Deep Chroma Extractor

TL;DR

This paper introduces a neural network-based learned chroma feature extractor that improves chord recognition accuracy by producing more robust harmonic features compared to traditional methods.

Contribution

It proposes a novel neural network approach to learn chroma features directly from audio spectra, enhancing robustness and harmonic information encoding for chord recognition.

Findings

Learned chroma features outperform hand-crafted ones in chord recognition.

The neural network effectively reduces noise and resolves harmonic ambiguities.

The approach improves accuracy across various datasets.

Abstract

We explore frame-level audio feature learning for chord recognition using artificial neural networks. We present the argument that chroma vectors potentially hold enough information to model harmonic content of audio for chord recognition, but that standard chroma extractors compute too noisy features. This leads us to propose a learned chroma feature extractor based on artificial neural networks. It is trained to compute chroma features that encode harmonic information important for chord recognition, while being robust to irrelevant interferences. We achieve this by feeding the network an audio spectrum with context instead of a single frame as input. This way, the network can learn to selectively compensate noise and resolve harmonic ambiguities. We compare the resulting features to hand-crafted ones by using a simple linear frame-wise classifier for chord recognition on various data sets. The results show that the learned feature extractor produces superior chroma vectors for chord recognition.